(De)Constructing Multiculturalism: A Discourse Analysis of Immigration and Refugee System in Canadian Media
Bibliographic record
Abstract
A succession of policy changes to the immigration and refugee system has been made in Canada in recent years by the Conservative federal government. Since most people’s understandings about immigration issues come from exposure to the news, the media have an important role in producing and reproducing prevalent public opinions to support and legitimize, or criticize, social and political actions. The present study examines how the immigration and refugee policy changes have been represented in mainstream print media and provides an important interface between recent political decision-making and society with regard to immigration issues. In our analysis, we demonstrate that there is a construction of the existing system as facing crisis due to rampant frauds to legitimize the implementation of more restrictive “get-tough” policies as pragmatic and commonsensical interventions. On the other hand, there is a privileging of framing immigration as being necessary for society, albeit in economic rather than sociocultural terms. In the media, social categorizations of immigrants into “good” and “bad,” and refugee claimants into “genuine” and “bogus,” are deployed to support the policy changes for a market-driven immigration system while restricting the admission of refugees and family-class immigrants, who are often portrayed as a burden on public resources.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.033 | 0.025 |
| Scholarly communication | 0.017 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".